Evaluation of two different semi-automated homogenization techniques in microbiological diagnosis of periprosthetic

Heime Rieber1, Andre Frontzek2, Stephanie Heinrich3

  • 1MVZ Dr. Stein and Colleagues, Division of Microbiology, Tomphecke 45, D-41169, Mönchengladbach, Germany. hrieber@labor-stein.de.

BMC Infectious Diseases
|October 17, 2022
PubMed
Abstract

Insights

Both high-frequency disperser and bead milling methods reliably process tissue samples for diagnosing periprosthetic joint infection (PJI). Sample volume and origin impact results, with bead milling showing promise for smaller biopsies in arthroscopic diagnosis.

Area of Science:

  • Microbiology
  • Orthopedic Surgery
  • Infectious Diseases

Background:

  • Consensus is lacking on optimal methods for microbiological diagnosis of periprosthetic joint infection (PJI).
  • Standardized techniques for tissue processing and sample volume are debated.
  • Accurate diagnosis is crucial for effective PJI management.

Purpose of the Study:

  • To compare the accuracy of two semi-automated tissue homogenization methods for PJI diagnosis.
  • To evaluate the influence of sample volume and origin on diagnostic yield.
  • To assess the suitability of these methods for routine clinical use.

Main Methods:

  • Comparative analysis of 722 periprosthetic tissue samples from 80 patients.
  • Evaluation of a high-frequency disperser versus a bead milling method for tissue processing.
  • PJI classification based on a new scoring system for preoperative and intraoperative criteria.

Main Results:

  • Both methods yielded reliable results, with 68% concordance.
  • Disperser and bead milling provided additional positive results in 20% and 12% of cases, respectively.
  • Bead milling showed better performance for smaller tissue samples, potentially improving arthroscopic diagnosis.

Conclusions:

  • Both high-frequency disperser and bead milling methods are suitable for reliable PJI diagnosis.
  • Tissue sample volume and origin are critical factors influencing diagnostic accuracy.
  • Further optimization of tissue processing techniques can enhance preoperative diagnosis.